scholarly journals The Dynamics of Language Network Interactions in Lexical Selection: An Intracranial EEG Study

2020 ◽  
Author(s):  
Yujing Wang ◽  
Anna Korzeniewska ◽  
Kiyohide Usami ◽  
Alyssandra Valenzuela ◽  
Nathan E Crone
Keyword(s):  
Temporal Cortex ◽  
Sentence Completion ◽  
Local Network ◽  
Lexical Selection ◽  
Population Activity ◽  
Intracranial Eeg ◽  
Lexical Representations ◽  
High Gamma ◽  
Gamma Power ◽  
Intracranial Recordings

Abstract Speaking in sentences requires selection from contextually determined lexical representations. Although posterior temporal cortex (PTC) and Broca’s areas play important roles in storage and selection, respectively, of lexical representations, there has been no direct evidence for physiological interactions between these areas on time scales typical of lexical selection. Using intracranial recordings of cortical population activity indexed by high-gamma power (70–150 Hz) modulations, we studied the causal dynamics of cortical language networks while epilepsy surgery patients performed a sentence completion task in which the number of potential lexical responses was systematically varied. Prior to completion of sentences with more response possibilities, Broca’s area was not only more active, but also exhibited more local network interactions with and greater top-down influences on PTC, consistent with activation of, and competition between, more lexical representations. These findings provide the most direct experimental support yet for network dynamics playing a role in lexical selection among competing alternatives during speech production.

scholarly journals Measurement of the mapping between intracranial EEG and fMRI recordings in the human brain

2017 ◽  
Author(s):  
DW Carmichael ◽  
S Vulliemoz ◽  
T Murta ◽  
U. Chaudhary ◽  
S Perani ◽  
...  
Keyword(s):  
Human Brain ◽  
Sensorimotor Cortex ◽  
Brain Activity ◽  
Spatial Scales ◽  
Electrophysiological Response ◽  
Intracranial Eeg ◽  
Eeg Activity ◽  
High Gamma ◽  
Gamma Power ◽  
Patients With Epilepsy

AbstractThere are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales by intracranial electroencephalography and fMRI. By comparing individual features and summary descriptions of intracranial EEG activity we determined which best predict fMRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. We also then examine the specificity of this relationship to spatial colocalisation of the two signals.We acquired electrocorticography and fMRI simultaneously (ECoG-fMRI) in the sensorimotor cortex of 3 patients with epilepsy. During motor activity, high gamma power was the only frequency band where the electrophysiological response was colocalised with fMRI measures across all subjects. The best model of fMRI changes was its principal components, a parsimonious description of the entire ECoG spectrogram. This model performed much better than a model based on the classical frequency bands both during task and rest periods or models derived on a summary of cross spectral changes (e.g. ‘root mean squared EEG frequency’). This suggests that the region specific fMRI signal is reflected in spatially and spectrally distributed EEG activity.

scholarly journals Including measures of high gamma power can improve the decoding of natural speech from EEG

2019 ◽  
Author(s):  
Shyanthony R. Synigal ◽  
Emily S. Teoh ◽  
Edmund C. Lalor
Keyword(s):  
Brain Imaging ◽  
Neural Activity ◽  
Speech Processing ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Natural Speech ◽  
Low Frequencies ◽  
High Gamma ◽  
Gamma Power ◽  
Speech Tracking

ABSTRACTThe human auditory system is adept at extracting information from speech in both single-speaker and multi-speaker situations. This involves neural processing at the rapid temporal scales seen in natural speech. Non-invasive brain imaging (electro-/magnetoencephalography [EEG/MEG]) signatures of such processing have shown that the phase of neural activity below 16 Hz tracks the dynamics of speech, whereas invasive brain imaging (electrocorticography [ECoG]) has shown that such rapid processing is even more strongly reflected in the power of neural activity at high frequencies (around 70-150 Hz; known as high gamma). The aim of this study was to determine if high gamma power in scalp recorded EEG carries useful stimulus-related information, despite its reputation for having a poor signal to noise ratio. Furthermore, we aimed to assess whether any such information might be complementary to that reflected in well-established low frequency EEG indices of speech processing. We used linear regression to investigate speech envelope and attention decoding in EEG at low frequencies, in high gamma power, and in both signals combined. While low frequency speech tracking was evident for almost all subjects as expected, high gamma power also showed robust speech tracking in a minority of subjects. This same pattern was true for attention decoding using a separate group of subjects who undertook a cocktail party attention experiment. For the subjects who showed speech tracking in high gamma power, the spatiotemporal characteristics of that high gamma tracking differed from that of low-frequency EEG. Furthermore, combining the two neural measures led to improved measures of speech tracking for several subjects. Overall, this indicates that high gamma power EEG can carry useful information regarding speech processing and attentional selection in some subjects and combining it with low frequency EEG can improve the mapping between natural speech and the resulting neural responses.

scholarly journals Direct cortical stimulation of human posteromedial cortex

Neurology ◽  
2017 ◽  
Vol 88 (7) ◽  
pp. 685-691 ◽  
Author(s):  
Brett L. Foster ◽  
Josef Parvizi
Keyword(s):  
Subjective Experience ◽  
Human Cognition ◽  
Intracranial Eeg ◽  
Autobiographical Recall ◽  
Electric Brain Stimulation ◽  
Intracranial Electrodes ◽  
Intracranial Recordings ◽  
The Brain ◽  
Epilepsy Monitoring ◽  
Human Awareness

Background:The posteromedial cortex (PMC) is a collective term for an anatomically heterogeneous area of the brain constituting a core node of the human default mode network (DMN), which is engaged during internally focused subjective cognition such as autobiographical memory.Methods:We explored the effects of causal perturbations of PMC with direct electric brain stimulation (EBS) during presurgical epilepsy monitoring with intracranial EEG electrodes.Results:Data were collected from 885 stimulations in 25 patients implanted with intracranial electrodes across the PMC. While EBS of regions immediately dorsal or ventral to the PMC reliably produced somatomotor or visual effects, respectively, we found no observable behavioral or subjectively reported effects when sites within the boundaries of PMC were electrically perturbed. In each patient, null effects of PMC stimulation were observed for sites in which intracranial recordings had clearly demonstrated electrophysiologic responses during autobiographical recall.Conclusions:Direct electric modulation of the human PMC produced null effects when standard functional mapping methods were used. More sophisticated stimulation paradigms (e.g., EBS during experimental cognitive tests) will be required for testing the causal contribution of PMC to human cognition and subjective experience. Nonetheless, our findings suggest that some extant theories of PMC and DMN contribution to human awareness and subjective conscious states require cautious re-examination.

scholarly journals Engineering Nonlinear Epileptic Biomarkers Using Deep Learning and Benford’s Law

2021 ◽  
Author(s):  
Joseph Caffarini ◽  
Klevest Gjini ◽  
Brinda Sevak ◽  
Roger Waleffe ◽  
Mariel Kalkach-Aparicio ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unique Feature ◽  
Single Channel ◽  
Seizure Detection ◽  
Intracranial Eeg ◽  
Feature Spaces ◽  
Frequency Domains ◽  
High Gamma ◽  
Leave One Out ◽  
Early Seizure

Abstract In this study we designed two deep neural networks to encode 16 feature latent spaces for early seizure detection in intracranial EEG and compared them to 16 widely used engineered metrics: Epileptogenicity Index (EI), Phase Locked High Gamma (PLHG), Time and Frequency Domain Cho Gaines Distance (TDCG, FDCG), relative band powers, and log absolute band powers (from alpha, beta, theta, delta, gamma, and high gamma bands. The deep learning models were pretrained for seizure identification on the time and frequency domains of one second single channel clips of 127 seizures (from 25 different subjects) using “leave-one-out” (LOO) cross validation. Each neural network extracted unique feature spaces that were used to train a Random Forest Classifier (RFC) for seizure identification and latency tasks. The Gini Importance of each feature was calculated from the pretrained RFC, enabling the most significant features (MSFs) for each task to be identified. The MSFs were extracted from the UPenn and Mayo Clinic's Seizure Detection Challenge to train another RFC for the contest. They obtained an AUC score of 0.93, demonstrating a transferable method to identify interpretable biomarkers for seizure detection.

scholarly journals Stratification of behavioral response to transcranial current stimulation by resting-state electrophysiology

2020 ◽  
Author(s):  
Atalanti A. Mastakouri
Keyword(s):  
Resting State ◽  
Behavioral Response ◽  
Prediction Method ◽  
Neurophysiological Mechanism ◽  
Reaching Task ◽  
State Activity ◽  
High Gamma ◽  
Spatial Configurations ◽  
Gamma Power ◽  
Transcranial Alternating Current Stimulation

Transcranial alternating current stimulation (tACS) enables the non-invasive stimulation of brain areas in desired frequencies, intensities and spatial configurations. These attributes have raised tACS to a widely used tool in cognitive neuroscience and a promising treatment in the field of motor rehabilitation. Nevertheless, considerable heterogeneity of its behavioral effects has been reported across individuals. We present a machine learning pipeline for predicting the behavioral response to 70 Hz contralateral motor cortex-tACS from Electroencephalographic resting-state activity preceding the stimulation. Specifically, we show in a cross-over study design that high-gamma (90--160 Hz) resting-state activity predicts arm-speed response to the stimulation in a concurrent reaching task. Moreover, we show in a prospective stimulation study that the behavioural effect size of stimulation significantly increases after the stratification of subjects with our prediction method. Finally, we discuss a plausible neurophysiological mechanism that links high resting-state gamma power in motor areas to stimulation response. As such, we provide a method that can distinguish responders from non-responders to tACS, prior to the stimulation treatment. This contribution could eventually bring us a step closer towards translating tACS into a safe and effective clinical treatment tool.

scholarly journals Differentiation of speech-induced artifacts from physiological high gamma activity in intracranial recordings

2021 ◽  
Author(s):  
Alan Bush ◽  
Anna Chrabaszcz ◽  
Victoria Peterson ◽  
Varun Saravanan ◽  
Christina Dastolfo-Hromack ◽  
...  
Keyword(s):  
Speech Production ◽  
Field Potential ◽  
Vibration Sensor ◽  
Gamma Activity ◽  
Time Frequency ◽  
Related Data ◽  
Syllable Repetition ◽  
Stereotactic Frame ◽  
High Gamma ◽  
Intracranial Recordings

AbstractThere is great interest in identifying the neurophysiological underpinnings of speech production. Deep brain stimulation (DBS) surgery is unique in that it allows intracranial recordings from both cortical and subcortical regions in patients who are awake and speaking. The quality of these recordings, however, may be affected to various degrees by mechanical forces resulting from speech itself. Here we describe the presence of speech-induced artifacts in local-field potential (LFP) recordings obtained from mapping electrodes, DBS leads, and cortical electrodes. In addition to expected physiological increases in high gamma (60-200 Hz) activity during speech production, time-frequency analysis in many channels revealed a narrowband gamma component that exhibited a pattern similar to that observed in the speech audio spectrogram. This component was present to different degrees in multiple types of neural recordings. We show that this component tracks the fundamental frequency of the participant’s voice, correlates with the power spectrum of speech and has coherence with the produced speech audio. A vibration sensor attached to the stereotactic frame recorded speech-induced vibrations with the same pattern observed in the LFPs. No corresponding component was identified in any neural channel during the listening epoch of a syllable repetition task. These observations demonstrate how speech-induced vibrations can create artifacts in the primary frequency band of interest. Identifying and accounting for these artifacts is crucial for establishing the validity and reproducibility of speech-related data obtained from intracranial recordings during DBS surgery.

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